Which of the following is the most common reason that business systems fail?

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BlackTom AI · Accepted Answer

To begin, I will restate what is provided: the question asks, 'Which of the following is the most common reason that business systems fail?' The available data includes an answer labeled as the correct option: 'Data quality', but there are no answer choices listed in the answer_options field.

Given the typical landscape of business systems failures, several broad categories are often discussed in literature and practitioner guidance, such as data quality issues, scope creep, poor requirements gathering, inadequate stakeholder involvement, integration complexity, and lack of executive sponsorship. When data quality is inadequate—containing inaccuracies, inconsistencies, duplicates, or missing values—it can undermine decision making, erode trust in the system, cause analytics to mislead, and propagate errors through workflows. Such problems can derail projects early or degrade system usefulness after deployment, making data quality a frequent and impactful root cause.

Now, since no alternative options are presented, I will analyze the presumed single option provided:
- Data quality: This choice points to problems with the input data or data management processes. If data is dirty, incomplete, or inconsistent, the system may fail to deliver reliable results, persist correct information, or operate as intended. In many case studies and post-implementation reviews, data quality is cited as a leading contributor to failed or underperforming systems because even a well-designed technical solution cannot compensate for poor data. The reasoning here hinges on the dependency of most business systems on accurate, complete, and trustworthy data; when data quality deteriorates, the value proposition of the system collapses.

If there were other answer options, one would compare them against this rationale:
- For example, if another choice suggested 'scope creep' or 'lack of user training', these are legitimate failure modes, but they often interact with or exacerbate data-related problems. A robust critique would examine whether such factors independently cause more frequent failures than data quality, or whether they typically amplify issues already rooted in data quality shortcomings.
- If an option claimed 'technology novelty' or 'vendor support' as the primary cause, one would evaluate how frequently those factors alone lead to project failure compared to data quality, which more directly affects system reliability and decision usefulness.

In summary, given the conventional emphasis in enterprise analytics and ERP-like projects on trustworthy data as foundational to any system’s success, data quality is often highlighted as a primary driver of failure when data is mishandled. The absence of additional options limits a full comparative analysis, but the cited reasoning for data quality remains a strong, widely recognized explanation for why business systems fail or underperform.

ISA201_D_202610 Final Exam Spring 2025

Which of the following is the most common reason that business systems fail?

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